
# Libraties for data manipulation and cleaning
import pandas as pd 
import numpy as np


# Libraries for visualization
import matplotlib.pyplot as plt
%matplotlib inline 
import seaborn as sns
import plotly.express as pe


# Loading csv files into designated dataframe

bluetooth_speakers  = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/bluetooth speakers.csv')
boat_product = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/BoatProduct.csv')
limited_edition = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/Limited Edition.csv')
misfit = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/Misfit.csv')
mobile_accessories = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/Mobile Accessories.csv')
smart_watches = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/smart watches.csv')
trebel = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/TRebel.csv')
wired_headphones = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/wired headphones.csv')
wireless_earbuds = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/wireless earbuds.csv')
wireless_headphones = pd.read_csv('E:/Data Analyst Project By self/#3 BoAts Product Review Analysis/#1 Original Dataset/wireless headphones.csv')


bluetooth_speakers


# Stage 1 : Dropping duplicates on basis of ProductName
bluetooth_speakers.drop_duplicates(subset = 'ProductName',inplace = True)
bluetooth_speakers['ProductName'].iloc[1]

# Stage 2 : Triming spaces in ProductName using strip
bluetooth_speakers['ProductName'] = bluetooth_speakers['ProductName'].str.strip()
bluetooth_speakers['ProductName'].iloc[1]

# Stage 3 : Cleaning ProductPrice column and making data type integer
product_price = bluetooth_speakers['ProductPrice'].str.split(' ',n=2,expand = True)
bluetooth_speakers['ProductPrice'] = product_price[2]
bluetooth_speakers['ProductPrice'] = bluetooth_speakers['ProductPrice'].str.replace(',','')
bluetooth_speakers
bluetooth_speakers['ProductPrice'] = bluetooth_speakers['ProductPrice'].astype(int)

# Stage 4 : Cleaning NumberofReviews and making data type as int
reviews = bluetooth_speakers['NumberofReviews'].str.split(' ',n=2,expand = True)
reviews = reviews[0]
bluetooth_speakers['NumberofReviews'] = reviews
bluetooth_speakers.rename(columns = {'NumberofReviews':'Reviews'},inplace = True)
bluetooth_speakers['Reviews'] = bluetooth_speakers['Reviews'].astype(int)

# Stage 5 : Cleaning Discount Column
discount= bluetooth_speakers['Discount'].str.split(' ',n=2,expand = True)
discount = discount[0]
bluetooth_speakers['Discount'] = discount

# Stage 6 : Cleaning Rate and making data type as float
rate = bluetooth_speakers['Rate'].str.split(' ', n=1 , expand = True)
rate = rate[1]
bluetooth_speakers['Rate'] = rate
bluetooth_speakers['Rate'] = bluetooth_speakers['Rate'].str.replace('\n','')
bluetooth_speakers['Rate'] = bluetooth_speakers['Rate'].str.strip()
bluetooth_speakers['Rate'] = bluetooth_speakers['Rate'].astype(float)

# Stage 7 : dropping Review and Summary columns
bluetooth_speakers.drop(columns = ['Review','Summary'],inplace = True)
bluetooth_speakers = bluetooth_speakers.set_index('ProductName').reset_index()


bluetooth_speakers


bluetooth_speakers.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   ProductName   10 non-null     object 
 1   ProductPrice  10 non-null     int32  
 2   Discount      10 non-null     object 
 3   Reviews       10 non-null     int32  
 4   Rate          10 non-null     float64
dtypes: float64(1), int32(2), object(2)
memory usage: 448.0+ bytes


bluetooth_speakers.describe()


limited_edition


# Stage 1 : Dropping duplicates on basis of ProductName
limited_edition.drop_duplicates(subset = 'ProductName',inplace = True)
limited_edition['ProductName'].iloc[1]

# Stage 2 : Triming spaces in ProductName using strip
limited_edition['ProductName'] = limited_edition['ProductName'].str.strip()
limited_edition['ProductName'].iloc[1]

# Stage 3 : Cleaning ProductPrice column and making data type integer
product_price = limited_edition['ProductPrice'].str.split(' ',n=2,expand = True)
limited_edition['ProductPrice'] = product_price[2]
limited_edition['ProductPrice'] = limited_edition['ProductPrice'].str.replace(',','')
limited_edition
limited_edition['ProductPrice'] = limited_edition['ProductPrice'].astype(int)

# Stage 4 : Cleaning NumberofReviews and making data type as int
reviews = limited_edition['NumberofReviews'].str.split(' ',n=2,expand = True)
reviews = reviews[0]
limited_edition['NumberofReviews'] = reviews
limited_edition.rename(columns = {'NumberofReviews':'Reviews'},inplace = True)
limited_edition['Reviews'] = limited_edition['Reviews'].astype(int)

# Stage 5 : Cleaning Discount Column
discount= limited_edition['Discount'].str.split(' ',n=2,expand = True)
discount = discount[0]
limited_edition['Discount'] = discount

# Stage 6 : Cleaning Rate and making data type as float
rate = limited_edition['Rate'].str.split(' ', n=1 , expand = True)
rate = rate[1]
limited_edition['Rate'] = rate
limited_edition['Rate'] = limited_edition['Rate'].str.replace('\n','')
limited_edition['Rate'] = limited_edition['Rate'].str.strip()
limited_edition['Rate'] = limited_edition['Rate'].astype(float)

# Stage 7 : dropping Review and Summary columns
limited_edition.drop(columns = ['Review','Summary'],inplace = True)
limited_edition = limited_edition.set_index('ProductName').reset_index()


limited_edition


limited_edition.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27 entries, 0 to 26
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   ProductName   27 non-null     object 
 1   ProductPrice  27 non-null     int32  
 2   Discount      27 non-null     object 
 3   Reviews       27 non-null     int32  
 4   Rate          27 non-null     float64
dtypes: float64(1), int32(2), object(2)
memory usage: 992.0+ bytes


misfit


# Stage 1 : Dropping duplicates on basis of ProductName
misfit.drop_duplicates(['ProductName'],inplace  = True)
misfit.set_index('ProductName').reset_index()

# Stage 2 : Triming spaces in ProductName using strip
misfit['ProductName']  = misfit['ProductName'].str.strip()
misfit['ProductName'].iloc[1]

# Stage 3 : Cleaning ProductPrice column and making data type integer
price = misfit['ProductPrice'].str.split(' ',n=2,expand = True)
price = price[2]
misfit['ProductPrice'] = price
misfit['ProductPrice'] = misfit['ProductPrice'].str.replace(',','') 
misfit['ProductPrice'] = misfit['ProductPrice'].astype(int)


# Stage 4 : Cleaning NumberofReviews and making data type as int
review = misfit['NumberofReviews'].str.split(' ',n=2,expand = True)
review = review[0]
misfit['NumberofReviews'] = review
misfit.rename(columns = {'NumberofReviews':'Reviews'},inplace = True)
misfit['Reviews'] = misfit['Reviews'].astype(int) 

# Stage 5 : Cleaning Discount Column
discount= misfit['Discount'].str.split(' ',n=2,expand = True)
discount = discount[0]
misfit['Discount'] = discount

# Stage 6 : Cleaning Rate and making data type as float
rate = misfit['Rate'].str.split(' ', n=1 , expand = True)
rate = rate[1]
misfit['Rate'] = rate
misfit['Rate'] = misfit['Rate'].str.replace('\n','')
misfit['Rate'] = misfit['Rate'].str.strip()
misfit['Rate'] = misfit['Rate'].astype(float)


# Stage 7 : dropping Review and Summary columns
misfit.drop(columns = ['Review','Summary'],inplace = True)
misfit = misfit.set_index('ProductName').reset_index()


misfit


misfit.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   ProductName   6 non-null      object 
 1   ProductPrice  6 non-null      int32  
 2   Discount      6 non-null      object 
 3   Reviews       6 non-null      int32  
 4   Rate          6 non-null      float64
dtypes: float64(1), int32(2), object(2)
memory usage: 320.0+ bytes


mobile_accessories


# Stage 1 : Dropping duplicates on basis of ProductName

mobile_accessories.drop_duplicates('ProductName',inplace = True)
mobile_accessories['ProductName'].iloc[1]

# Stage 2 : Triming spaces in ProductName using strip
mobile_accessories['ProductName'] = mobile_accessories['ProductName'].str.strip()
mobile_accessories['ProductName'].iloc[1]

# Stage 3 : Cleaning ProductPrice column and making data type integer
product_price = mobile_accessories['ProductPrice'].str.split(' ',n=2,expand = True)
mobile_accessories['ProductPrice'] = product_price[2]
mobile_accessories['ProductPrice'] = mobile_accessories['ProductPrice'].str.replace(',','')
mobile_accessories
mobile_accessories['ProductPrice'] = mobile_accessories['ProductPrice'].astype(int)

# Stage 4 : Cleaning NumberofReviews and making data type as int
reviews = mobile_accessories['NumberofReviews'].str.split(' ',n=2,expand = True)
reviews = reviews[0]
mobile_accessories['NumberofReviews'] = reviews
mobile_accessories.rename(columns = {'NumberofReviews':'Reviews'},inplace = True)
mobile_accessories['Reviews'] = mobile_accessories['Reviews'].astype(int)

# Stage 5 : Cleaning Discount Column
discount= mobile_accessories['Discount'].str.split(' ',n=2,expand = True)
discount = discount[0]
mobile_accessories['Discount'] = discount

# Stage 6 : Cleaning Rate and making data type as float
rate = mobile_accessories['Rate'].str.split(' ', n=1 , expand = True)
rate = rate[1]
mobile_accessories['Rate'] = rate
mobile_accessories['Rate'] = mobile_accessories['Rate'].str.replace('\n','')
mobile_accessories['Rate'] = mobile_accessories['Rate'].str.strip()
mobile_accessories['Rate'] = mobile_accessories['Rate'].astype(float)

# Stage 7 : dropping Review and Summary columns
mobile_accessories.drop(columns = ['Review','Summary'],inplace = True)
mobile_accessories = mobile_accessories.set_index('ProductName').reset_index()


mobile_accessories


mobile_accessories.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16 entries, 0 to 15
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   ProductName   16 non-null     object 
 1   ProductPrice  16 non-null     int32  
 2   Discount      16 non-null     object 
 3   Reviews       16 non-null     int32  
 4   Rate          16 non-null     float64
dtypes: float64(1), int32(2), object(2)
memory usage: 640.0+ bytes


smart_watches


# Stage 1 : Dropping duplicates on basis of ProductName
smart_watches.drop_duplicates(subset = 'ProductName',inplace = True)
smart_watches['ProductName'].iloc[1]

# Stage 2 : Triming spaces in ProductName using strip
smart_watches['ProductName'] = smart_watches['ProductName'].str.strip()
smart_watches['ProductName'].iloc[1]

# Stage 3 : Cleaning ProductPrice column and making data type integer
product_price = smart_watches['ProductPrice'].str.split(' ',n=2,expand = True)
smart_watches['ProductPrice'] = product_price[2]
smart_watches['ProductPrice'] = smart_watches['ProductPrice'].str.replace(',','')
smart_watches
smart_watches['ProductPrice'] = smart_watches['ProductPrice'].astype(int)

# Stage 4 : Cleaning NumberofReviews and making data type as int
reviews = smart_watches['NumberofReviews'].str.split(' ',n=2,expand = True)
reviews = reviews[0]
smart_watches['NumberofReviews'] = reviews
smart_watches.rename(columns = {'NumberofReviews':'Reviews'},inplace = True)
smart_watches['Reviews'] = smart_watches['Reviews'].astype(int)

# Stage 5 : Cleaning Discount Column
discount= smart_watches['Discount'].str.split(' ',n=2,expand = True)
discount = discount[0]
smart_watches['Discount'] = discount

# Stage 6 : Cleaning Rate and making data type as float
rate = smart_watches['Rate'].str.split(' ', n=1 , expand = True)
rate = rate[1]
smart_watches['Rate'] = rate
smart_watches['Rate'] = smart_watches['Rate'].str.replace('\n','')
smart_watches['Rate'] = smart_watches['Rate'].str.strip()
smart_watches['Rate'] = smart_watches['Rate'].astype(float)

# Stage 7 : dropping Review and Summary columns
smart_watches.drop(columns = ['Review','Summary'],inplace = True)
smart_watches = smart_watches.set_index('ProductName').reset_index()


smart_watches


smart_watches.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 31 entries, 0 to 30
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   ProductName   31 non-null     object 
 1   ProductPrice  31 non-null     int32  
 2   Discount      31 non-null     object 
 3   Reviews       31 non-null     int32  
 4   Rate          31 non-null     float64
dtypes: float64(1), int32(2), object(2)
memory usage: 1.1+ KB


trebel


# Stage 1 : Dropping duplicates on basis of ProductName
trebel.drop_duplicates(subset = 'ProductName',inplace = True)
trebel['ProductName'].iloc[1]

# Stage 2 : Triming spaces in ProductName using strip
trebel['ProductName'] = trebel['ProductName'].str.strip()
trebel['ProductName'].iloc[1]

# Stage 3 : Cleaning ProductPrice column and making data type integer
product_price = trebel['ProductPrice'].str.split(' ',n=2,expand = True)
trebel['ProductPrice'] = product_price[2]
trebel['ProductPrice'] = trebel['ProductPrice'].str.replace(',','')
trebel
trebel['ProductPrice'] = trebel['ProductPrice'].astype(int)

# Stage 4 : Cleaning NumberofReviews and making data type as int
reviews = trebel['NumberofReviews'].str.split(' ',n=2,expand = True)
reviews = reviews[0]
trebel['NumberofReviews'] = reviews
trebel.rename(columns = {'NumberofReviews':'Reviews'},inplace = True)
trebel['Reviews'] = trebel['Reviews'].astype(int)

# Stage 5 : Cleaning Discount Column
discount= trebel['Discount'].str.split(' ',n=2,expand = True)
discount = discount[0]
trebel['Discount'] = discount

# Stage 6 : Cleaning Rate and making data type as float
rate = trebel['Rate'].str.split(' ', n=1 , expand = True)
rate = rate[1]
trebel['Rate'] = rate
trebel['Rate'] = trebel['Rate'].str.replace('\n','')
trebel['Rate'] = trebel['Rate'].str.strip()
trebel['Rate'] = trebel['Rate'].astype(float)

# Stage 7 : dropping Review and Summary columns
trebel.drop(columns = ['Review','Summary'],inplace = True)
trebel = trebel.set_index('ProductName').reset_index()


trebel


trebel.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21 entries, 0 to 20
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   ProductName   21 non-null     object 
 1   ProductPrice  21 non-null     int32  
 2   Discount      21 non-null     object 
 3   Reviews       21 non-null     int32  
 4   Rate          21 non-null     float64
dtypes: float64(1), int32(2), object(2)
memory usage: 800.0+ bytes


wired_headphones


# Stage 1 : Dropping duplicates on basis of ProductName
wired_headphones.drop_duplicates(subset = 'ProductName',inplace = True)
wired_headphones['ProductName'].iloc[1]

# Stage 2 : Triming spaces in ProductName using strip
wired_headphones['ProductName'] = wired_headphones['ProductName'].str.strip()
wired_headphones['ProductName'].iloc[1]

# Stage 3 : Cleaning ProductPrice column and making data type integer
product_price = wired_headphones['ProductPrice'].str.split(' ',n=2,expand = True)
wired_headphones['ProductPrice'] = product_price[2]
wired_headphones['ProductPrice'] = wired_headphones['ProductPrice'].str.replace(',','')
wired_headphones
wired_headphones['ProductPrice'] = wired_headphones['ProductPrice'].astype(int)

# Stage 4 : Cleaning NumberofReviews and making data type as int
reviews = wired_headphones['NumberofReviews'].str.split(' ',n=2,expand = True)
reviews = reviews[0]
wired_headphones['NumberofReviews'] = reviews
wired_headphones.rename(columns = {'NumberofReviews':'Reviews'},inplace = True)
wired_headphones['Reviews'] = wired_headphones['Reviews'].astype(int)

# Stage 5 : Cleaning Discount Column
discount= wired_headphones['Discount'].str.split(' ',n=2,expand = True)
discount = discount[0]
wired_headphones['Discount'] = discount

# Stage 6 : Cleaning Rate and making data type as float
rate = wired_headphones['Rate'].str.split(' ', n=1 , expand = True)
rate = rate[1]
wired_headphones['Rate'] = rate
wired_headphones['Rate'] = wired_headphones['Rate'].str.replace('\n','')
wired_headphones['Rate'] = wired_headphones['Rate'].str.strip()
wired_headphones['Rate'] = wired_headphones['Rate'].astype(float)

# Stage 7 : dropping Review and Summary columns
wired_headphones.drop(columns = ['Review','Summary'],inplace = True)
wired_headphones = wired_headphones.set_index('ProductName').reset_index()


wired_headphones


wired_headphones.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18 entries, 0 to 17
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   ProductName   18 non-null     object 
 1   ProductPrice  18 non-null     int32  
 2   Discount      18 non-null     object 
 3   Reviews       18 non-null     int32  
 4   Rate          18 non-null     float64
dtypes: float64(1), int32(2), object(2)
memory usage: 704.0+ bytes


wireless_earbuds


# Stage 1 : Dropping duplicates on basis of ProductName
wireless_earbuds.drop_duplicates(subset = 'ProductName',inplace = True)
wireless_earbuds['ProductName'].iloc[1]

# Stage 2 : Triming spaces in ProductName using strip
wireless_earbuds['ProductName'] = wireless_earbuds['ProductName'].str.strip()
wireless_earbuds['ProductName'].iloc[1]

# Stage 3 : Cleaning ProductPrice column and making data type integer
product_price = wireless_earbuds['ProductPrice'].str.split(' ',n=2,expand = True)
wireless_earbuds['ProductPrice'] = product_price[2]
wireless_earbuds['ProductPrice'] = wireless_earbuds['ProductPrice'].str.replace(',','')
wireless_earbuds
wireless_earbuds['ProductPrice'] = wireless_earbuds['ProductPrice'].astype(int)

# Stage 4 : Cleaning NumberofReviews and making data type as int
reviews = wireless_earbuds['NumberofReviews'].str.split(' ',n=2,expand = True)
reviews = reviews[0]
wireless_earbuds['NumberofReviews'] = reviews
wireless_earbuds.rename(columns = {'NumberofReviews':'Reviews'},inplace = True)
wireless_earbuds['Reviews'] = wireless_earbuds['Reviews'].astype(int)

# Stage 5 : Cleaning Discount Column
discount= wireless_earbuds['Discount'].str.split(' ',n=2,expand = True)
discount = discount[0]
wireless_earbuds['Discount'] = discount

# Stage 6 : Cleaning Rate and making data type as float
rate = wireless_earbuds['Rate'].str.split(' ', n=1 , expand = True)
rate = rate[1]
wireless_earbuds['Rate'] = rate
wireless_earbuds['Rate'] = wireless_earbuds['Rate'].str.replace('\n','')
wireless_earbuds['Rate'] = wireless_earbuds['Rate'].str.strip()
wireless_earbuds['Rate'] = wireless_earbuds['Rate'].astype(float)

# Stage 7 : dropping Review and Summary columns
wireless_earbuds.drop(columns = ['Review','Summary'],inplace = True)
wireless_earbuds = wireless_earbuds.set_index('ProductName').reset_index()


wireless_earbuds


wireless_earbuds.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27 entries, 0 to 26
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   ProductName   27 non-null     object 
 1   ProductPrice  27 non-null     int32  
 2   Discount      27 non-null     object 
 3   Reviews       27 non-null     int32  
 4   Rate          27 non-null     float64
dtypes: float64(1), int32(2), object(2)
memory usage: 992.0+ bytes


wireless_headphones


# Stage 1 : Dropping duplicates on basis of ProductName
wireless_headphones.drop_duplicates(subset = 'ProductName',inplace = True)
wireless_headphones['ProductName'].iloc[1]

# Stage 2 : Triming spaces in ProductName using strip
wireless_headphones['ProductName'] = wireless_headphones['ProductName'].str.strip()
wireless_headphones['ProductName'].iloc[1]

# Stage 3 : Cleaning ProductPrice column and making data type integer
product_price = wireless_headphones['ProductPrice'].str.split(' ',n=2,expand = True)
wireless_headphones['ProductPrice'] = product_price[2]
wireless_headphones['ProductPrice'] = wireless_headphones['ProductPrice'].str.replace(',','')
wireless_headphones
wireless_headphones['ProductPrice'] = wireless_headphones['ProductPrice'].astype(int)

# Stage 4 : Cleaning NumberofReviews and making data type as int
reviews = wireless_headphones['NumberofReviews'].str.split(' ',n=2,expand = True)
reviews = reviews[0]
wireless_headphones['NumberofReviews'] = reviews
wireless_headphones.rename(columns = {'NumberofReviews':'Reviews'},inplace = True)
wireless_headphones['Reviews'] = wireless_headphones['Reviews'].astype(int)

# Stage 5 : Cleaning Discount Column
discount= wireless_headphones['Discount'].str.split(' ',n=2,expand = True)
discount = discount[0]
wireless_headphones['Discount'] = discount

# Stage 6 : Cleaning Rate and making data type as float
rate = wireless_headphones['Rate'].str.split(' ', n=1 , expand = True)
rate = rate[1]
wireless_headphones['Rate'] = rate
wireless_headphones['Rate'] = wireless_headphones['Rate'].str.replace('\n','')
wireless_headphones['Rate'] = wireless_headphones['Rate'].str.strip()
wireless_headphones['Rate'] = wireless_headphones['Rate'].astype(float)

# Stage 7 : dropping Review and Summary columns
wireless_headphones.drop(columns = ['Review','Summary'],inplace = True)
wireless_headphones = wireless_headphones.set_index('ProductName').reset_index()


wireless_headphones


wireless_headphones.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 40 entries, 0 to 39
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   ProductName   40 non-null     object 
 1   ProductPrice  40 non-null     int32  
 2   Discount      40 non-null     object 
 3   Reviews       40 non-null     int32  
 4   Rate          40 non-null     float64
dtypes: float64(1), int32(2), object(2)
memory usage: 1.4+ KB


# bluetooth_speakers
most_reviews_discount_1 = bluetooth_speakers.sort_values(['Reviews','Discount'],ascending = False).head()
most_reviews_discount_1 = most_reviews_discount_1.set_index('ProductName').reset_index()
most_reviews_discount_1
product_type = {'Product_Type':['Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers',]}
product_type = pd.DataFrame(product_type)
most_reviews_discount_1['ProductType'] = product_type
most_reviews_discount_1

# limited_edition
most_reviews_discount_2 = limited_edition.sort_values(['Reviews','Discount'],ascending = False).head()
most_reviews_discount_2 = most_reviews_discount_2.set_index('ProductName').reset_index()
most_reviews_discount_2
product_type = {'Product_Type':['Limited Edition','Limited Edition','Limited Edition','Limited Edition','Limited Edition']}
product_type = pd.DataFrame(product_type)
most_reviews_discount_2['ProductType'] = product_type
most_reviews_discount_2

# misfit
most_reviews_discount_3 = misfit.sort_values(['Reviews','Discount'],ascending = False).head()
most_reviews_discount_3 = most_reviews_discount_3.set_index('ProductName').reset_index()
most_reviews_discount_3
product_type = {'Product_Type':['Misfit','Misfit','Misfit','Misfit','Misfit']}
product_type = pd.DataFrame(product_type)
most_reviews_discount_3['ProductType'] = product_type
most_reviews_discount_3

# mobile_accessories
most_reviews_discount_4 = mobile_accessories.sort_values(['Reviews','Discount'],ascending = False).head()
most_reviews_discount_4 = most_reviews_discount_4.set_index('ProductName').reset_index()
most_reviews_discount_4
product_type = {'Product_Type':['Mobile Accessories','Mobile Accessories','Mobile Accessories','Mobile Accessories','Mobile Accessories']}
product_type = pd.DataFrame(product_type)
most_reviews_discount_4['ProductType'] = product_type
most_reviews_discount_4

# smart_watches
most_reviews_discount_5 = smart_watches.sort_values(['Reviews','Discount'],ascending = False).head()
most_reviews_discount_5 = most_reviews_discount_5.set_index('ProductName').reset_index()
most_reviews_discount_5
product_type = {'Product_Type':['Smart Watches','Smart Watches','Smart Watches','Smart Watches','Smart Watches']}
product_type = pd.DataFrame(product_type)
most_reviews_discount_5['ProductType'] = product_type
most_reviews_discount_5

# trebel
most_reviews_discount_6 = trebel.sort_values(['Reviews','Discount'],ascending = False).head()
most_reviews_discount_6 = most_reviews_discount_6.set_index('ProductName').reset_index()
most_reviews_discount_6
product_type = {'Product_Type':['TRebel','TRebel','TRebel','TRebel','TRebel',]}
product_type = pd.DataFrame(product_type)
most_reviews_discount_6['ProductType'] = product_type
most_reviews_discount_6

# wired_headphones
most_reviews_discount_7 = wired_headphones.sort_values(['Reviews','Discount'],ascending = False).head()
most_reviews_discount_7 = most_reviews_discount_7.set_index('ProductName').reset_index()
most_reviews_discount_7
product_type = {'Product_Type':['Wired Headphones','Wired Headphones','Wired Headphones','Wired Headphones','Wired Headphones']}
product_type = pd.DataFrame(product_type)
most_reviews_discount_7['ProductType'] = product_type
most_reviews_discount_7

# wireless_earbuds
most_reviews_discount_8 = wireless_earbuds.sort_values(['Reviews','Discount'],ascending = False).head()
most_reviews_discount_8 = most_reviews_discount_8.set_index('ProductName').reset_index()
most_reviews_discount_8
product_type = {'Product_Type':['Wireless Earbuds','Wireless Earbuds','Wireless Earbuds','Wireless Earbuds','Wireless Earbuds']}
product_type = pd.DataFrame(product_type)
most_reviews_discount_8['ProductType'] = product_type
most_reviews_discount_8

# wireless_headphones
most_reviews_discount_9 = wireless_headphones['Discount'].dropna()
most_reviews_discount_9 = wireless_headphones.sort_values(['Reviews','Discount'],ascending = False).head()
most_reviews_discount_9 = most_reviews_discount_9.set_index('ProductName').reset_index()
most_reviews_discount_9
product_type = {'Product_Type':['Wireless Headphones','Wireless Headphones','Wireless Headphones','Wireless Headphones','Wireless Headphones']}
product_type = pd.DataFrame(product_type)
most_reviews_discount_9['ProductType'] = product_type
most_reviews_discount_9


most_reviews_discount_product = pd.concat([most_reviews_discount_1,most_reviews_discount_2,most_reviews_discount_3,most_reviews_discount_4,most_reviews_discount_5,most_reviews_discount_6,most_reviews_discount_7,most_reviews_discount_8,most_reviews_discount_9])


most_reviews_discount_product = most_reviews_discount_product.sort_values(['Reviews','Discount'],ascending =False).head(20)
most_reviews_discount_product.set_index('ProductName').reset_index()


most_reviews_discount_product = most_reviews_discount_product.groupby('ProductType').count().iloc[:,[0]]


most_reviews_discount_product.rename(columns = {'ProductName':'Total Products'},inplace = True)
most_reviews_discount_product =  most_reviews_discount_product.reset_index()
most_reviews_discount_product


# Figure Size
plt.figure(figsize=(10,5))
print(' ')

# For Graph Title
plt.title('Different Products Segment that has highest percentage of Discount and Reviews',
         fontsize = 15,
         fontweight = 'bold',
         fontname = 'Times New Roman')

print('  ')

# For plotting Pie Chart
plt.pie(x = most_reviews_discount_product['Total Products'],
        autopct = '%1.1f%%',
        explode = [0.1,0.1,0.1,0.1,0.1,0.1],
       labels = most_reviews_discount_product['ProductType'],
       colors = ['lightskyblue']);


# bluetooth_speakers
top_5_rated_products_1 = bluetooth_speakers.sort_values(['Rate','Reviews'],ascending = False).head()
top_5_rated_products_1 = top_5_rated_products_1.set_index('ProductName').reset_index()
top_5_rated_products_1
product_type = {'Product_Type':['Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers',]}
product_type = pd.DataFrame(product_type)
top_5_rated_products_1['ProductType'] = product_type
top_5_rated_products_1

# limited_edition
top_5_rated_products_2 = limited_edition.sort_values(['Rate','Reviews'],ascending = False).head()
top_5_rated_products_2 = top_5_rated_products_2.set_index('ProductName').reset_index()
top_5_rated_products_2
product_type = {'Product_Type':['Limited Edition','Limited Edition','Limited Edition','Limited Edition','Limited Edition']}
product_type = pd.DataFrame(product_type)
top_5_rated_products_2['ProductType'] = product_type
top_5_rated_products_2

# misfit
top_5_rated_products_3 = misfit.sort_values(['Rate','Reviews'],ascending = False).head()
top_5_rated_products_3 = top_5_rated_products_3.set_index('ProductName').reset_index()
top_5_rated_products_3
product_type = {'Product_Type':['Misfit','Misfit','Misfit','Misfit','Misfit']}
product_type = pd.DataFrame(product_type)
top_5_rated_products_3['ProductType'] = product_type
top_5_rated_products_3

# mobile_accessories
top_5_rated_products_4 = mobile_accessories.sort_values(['Rate','Reviews'],ascending = False).head()
top_5_rated_products_4 = top_5_rated_products_4.set_index('ProductName').reset_index()
top_5_rated_products_4
product_type = {'Product_Type':['Mobile Accessories','Mobile Accessories','Mobile Accessories','Mobile Accessories','Mobile Accessories']}
product_type = pd.DataFrame(product_type)
top_5_rated_products_4['ProductType'] = product_type
top_5_rated_products_4

# smart_watches
top_5_rated_products_5 = smart_watches.sort_values(['Rate','Reviews'],ascending = False).head()
top_5_rated_products_5 = top_5_rated_products_5.set_index('ProductName').reset_index()
top_5_rated_products_5
product_type = {'Product_Type':['Smart Watches','Smart Watches','Smart Watches','Smart Watches','Smart Watches']}
product_type = pd.DataFrame(product_type)
top_5_rated_products_5['ProductType'] = product_type
top_5_rated_products_5

# trebel
top_5_rated_products_6 = trebel.sort_values(['Reviews','Discount'],ascending = False).head()
top_5_rated_products_6 = top_5_rated_products_6.set_index('ProductName').reset_index()
top_5_rated_products_6
product_type = {'Product_Type':['TRebel','TRebel','TRebel','TRebel','TRebel',]}
product_type = pd.DataFrame(product_type)
top_5_rated_products_6['ProductType'] = product_type
top_5_rated_products_6

# wired_headphones
top_5_rated_products_7 = wired_headphones.sort_values(['Rate','Reviews'],ascending = False).head()
top_5_rated_products_7 = top_5_rated_products_7.set_index('ProductName').reset_index()
top_5_rated_products_7
product_type = {'Product_Type':['Wired Headphones','Wired Headphones','Wired Headphones','Wired Headphones','Wired Headphones']}
product_type = pd.DataFrame(product_type)
top_5_rated_products_7['ProductType'] = product_type
top_5_rated_products_7

# wireless_earbuds
top_5_rated_products_8 = wireless_earbuds.sort_values(['Rate','Reviews'],ascending = False).head()
top_5_rated_products_8 = top_5_rated_products_8.set_index('ProductName').reset_index()
top_5_rated_products_8
product_type = {'Product_Type':['Wireless Earbuds','Wireless Earbuds','Wireless Earbuds','Wireless Earbuds','Wireless Earbuds']}
product_type = pd.DataFrame(product_type)
top_5_rated_products_8['ProductType'] = product_type
top_5_rated_products_8

# wireless_headphones
top_5_rated_products_9 = wireless_headphones['Discount'].dropna()
top_5_rated_products_9 = wireless_headphones.sort_values(['Rate','Reviews'],ascending = False).head()
top_5_rated_products_9 = top_5_rated_products_9.set_index('ProductName').reset_index()
top_5_rated_products_9
product_type = {'Product_Type':['Wireless Headphones','Wireless Headphones','Wireless Headphones','Wireless Headphones','Wireless Headphones']}
product_type = pd.DataFrame(product_type)
top_5_rated_products_9['ProductType'] = product_type


top_5_rated_products = pd.concat([top_5_rated_products_1,top_5_rated_products_2,top_5_rated_products_3,top_5_rated_products_4,top_5_rated_products_5,top_5_rated_products_6,top_5_rated_products_7,top_5_rated_products_8,top_5_rated_products_9])


top_5_rated_products = top_5_rated_products.sort_values(['Rate','Reviews'],ascending = False)
top_5_rated_products = top_5_rated_products.where(top_5_rated_products['Reviews'] > 10).dropna()
top_5_rated_products = top_5_rated_products.set_index('ProductName').reset_index().head(10)
top_5_rated_products = top_5_rated_products.iloc[:,[0,3,4,5]]
top_5_rated_products


# Figure Size
plt.figure(figsize=(20,6))

# Plotting Barplot
ax = sns.barplot(data = top_5_rated_products,
            x = 'ProductName',
            y = 'Reviews',
           palette = 'cool')

# For Labels
ax.bar_label(ax.containers[0])

# X-axis Customization
plt.xticks(rotation = 45,fontsize = 13)
plt.xlabel('', fontsize = 20)

# y-axis Customization
plt.yticks(fontsize = 13)
plt.ylabel('', fontsize = 20)

# Increasing Data Labels Size
sns.set(font_scale = 1.2)

# Title
plt.title("Highest Number of Reviews by Product Names",fontsize= 25);


# bluetooth_speakers
highest_reviews_1 = bluetooth_speakers.sort_values(['Reviews'],ascending = False).head()
highest_reviews_1 = highest_reviews_1.set_index('ProductName').reset_index()
highest_reviews_1 
product_type = {'Product_Type':['Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers',]}
product_type = pd.DataFrame(product_type)
highest_reviews_1 ['ProductType'] = product_type
highest_reviews_1 

# limited_edition
highest_reviews_2= limited_edition.sort_values(['Reviews'],ascending = False).head()
highest_reviews_2= highest_reviews_2.set_index('ProductName').reset_index()
highest_reviews_2
product_type = {'Product_Type':['Limited Edition','Limited Edition','Limited Edition','Limited Edition','Limited Edition']}
product_type = pd.DataFrame(product_type)
highest_reviews_2['ProductType'] = product_type
highest_reviews_2


# misfit
highest_reviews_3 = misfit.sort_values(['Reviews'],ascending = False).head()
highest_reviews_3 = highest_reviews_3.set_index('ProductName').reset_index()
highest_reviews_3
product_type = {'Product_Type':['Misfit','Misfit','Misfit','Misfit','Misfit']}
product_type = pd.DataFrame(product_type)
highest_reviews_3['ProductType'] = product_type
highest_reviews_3


# mobile_accessories
highest_reviews_4 = mobile_accessories.sort_values(['Reviews'],ascending = False).head()
highest_reviews_4 = highest_reviews_4.set_index('ProductName').reset_index()
highest_reviews_4
product_type = {'Product_Type':['Mobile Accessories','Mobile Accessories','Mobile Accessories','Mobile Accessories','Mobile Accessories']}
product_type = pd.DataFrame(product_type)
highest_reviews_4['ProductType'] = product_type
highest_reviews_4

# smart_watches
highest_reviews_5 = smart_watches.sort_values(['Reviews'],ascending = False).head()
highest_reviews_5 = highest_reviews_5.set_index('ProductName').reset_index()
highest_reviews_5
product_type = {'Product_Type':['Smart Watches','Smart Watches','Smart Watches','Smart Watches','Smart Watches']}
product_type = pd.DataFrame(product_type)
highest_reviews_5['ProductType'] = product_type
highest_reviews_5


# trebel
highest_reviews_6 = trebel.sort_values(['Reviews'],ascending = False).head()
highest_reviews_6 = highest_reviews_6.set_index('ProductName').reset_index()
highest_reviews_6
product_type = {'Product_Type':['TRebel','TRebel','TRebel','TRebel','TRebel',]}
product_type = pd.DataFrame(product_type)
highest_reviews_6['ProductType'] = product_type
highest_reviews_6


# wired_headphones
highest_reviews_7 = wired_headphones.sort_values(['Reviews'],ascending = False).head()
highest_reviews_7 = highest_reviews_7.set_index('ProductName').reset_index()
highest_reviews_7
product_type = {'Product_Type':['Wired Headphones','Wired Headphones','Wired Headphones','Wired Headphones','Wired Headphones']}
product_type = pd.DataFrame(product_type)
highest_reviews_7['ProductType'] = product_type
highest_reviews_7

# wireless_earbuds
highest_reviews_8 = wireless_earbuds.sort_values(['Reviews'],ascending = False).head()
highest_reviews_8 = highest_reviews_8.set_index('ProductName').reset_index()
highest_reviews_8
product_type = {'Product_Type':['Wireless Earbuds','Wireless Earbuds','Wireless Earbuds','Wireless Earbuds','Wireless Earbuds']}
product_type = pd.DataFrame(product_type)
highest_reviews_8['ProductType'] = product_type
highest_reviews_8

# wireless_headphones
highest_reviews_9 = wireless_headphones['Discount'].dropna()
highest_reviews_9 = wireless_headphones.sort_values(['Reviews'],ascending = False).head()
highest_reviews_9 = highest_reviews_9.set_index('ProductName').reset_index()
highest_reviews_9
product_type = {'Product_Type':['Wireless Headphones','Wireless Headphones','Wireless Headphones','Wireless Headphones','Wireless Headphones']}
product_type = pd.DataFrame(product_type)
highest_reviews_9['ProductType'] = product_type


highest_reviews = pd.concat([highest_reviews_1,highest_reviews_2,highest_reviews_3,highest_reviews_4,highest_reviews_5,highest_reviews_6,highest_reviews_7,highest_reviews_8,highest_reviews_9])


highest_reviews = highest_reviews.sort_values('Reviews',ascending = False).set_index('ProductName').reset_index()
highest_reviews.head(10)


# bluetooth_speakers
bottom_5_rated_products_1 = bluetooth_speakers.sort_values(['Rate'],ascending = True).head()
bottom_5_rated_products_1 = bottom_5_rated_products_1.set_index('ProductName').reset_index()
bottom_5_rated_products_1
product_type = {'Product_Type':['Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers','Bluetooth Speakers',]}
product_type = pd.DataFrame(product_type)
bottom_5_rated_products_1['ProductType'] = product_type
bottom_5_rated_products_1

# limited_edition
bottom_5_rated_products_2 = limited_edition.sort_values(['Rate'],ascending = True).head()
bottom_5_rated_products_2 = bottom_5_rated_products_2.set_index('ProductName').reset_index()
bottom_5_rated_products_2
product_type = {'Product_Type':['Limited Edition','Limited Edition','Limited Edition','Limited Edition','Limited Edition']}
product_type = pd.DataFrame(product_type)
bottom_5_rated_products_2['ProductType'] = product_type
bottom_5_rated_products_2

# misfit
bottom_5_rated_products_3 = misfit.sort_values(['Rate'],ascending = True).head()
bottom_5_rated_products_3 = bottom_5_rated_products_3.set_index('ProductName').reset_index()
bottom_5_rated_products_3
product_type = {'Product_Type':['Misfit','Misfit','Misfit','Misfit','Misfit']}
product_type = pd.DataFrame(product_type)
bottom_5_rated_products_3['ProductType'] = product_type
bottom_5_rated_products_3

# mobile_accessories
bottom_5_rated_products_4 = mobile_accessories.sort_values(['Rate'],ascending = True).head()
bottom_5_rated_products_4 = bottom_5_rated_products_4.set_index('ProductName').reset_index()
bottom_5_rated_products_4
product_type = {'Product_Type':['Mobile Accessories','Mobile Accessories','Mobile Accessories','Mobile Accessories','Mobile Accessories']}
product_type = pd.DataFrame(product_type)
bottom_5_rated_products_4['ProductType'] = product_type
bottom_5_rated_products_4

# smart_watches
bottom_5_rated_products_5 = smart_watches.sort_values(['Rate'],ascending = True).head()
bottom_5_rated_products_5 = bottom_5_rated_products_5.set_index('ProductName').reset_index()
bottom_5_rated_products_5
product_type = {'Product_Type':['Smart Watches','Smart Watches','Smart Watches','Smart Watches','Smart Watches']}
product_type = pd.DataFrame(product_type)
bottom_5_rated_products_5['ProductType'] = product_type
bottom_5_rated_products_5

# trebel
bottom_5_rated_products_6 = trebel.sort_values(['Rate'],ascending = True).head()
bottom_5_rated_products_6 = bottom_5_rated_products_6.set_index('ProductName').reset_index()
bottom_5_rated_products_6
product_type = {'Product_Type':['TRebel','TRebel','TRebel','TRebel','TRebel',]}
product_type = pd.DataFrame(product_type)
bottom_5_rated_products_6['ProductType'] = product_type
bottom_5_rated_products_6

# wired_headphones
bottom_5_rated_products_7 = wired_headphones.sort_values(['Rate'],ascending = True).head()
bottom_5_rated_products_7 = bottom_5_rated_products_7.set_index('ProductName').reset_index()
bottom_5_rated_products_7
product_type = {'Product_Type':['Wired Headphones','Wired Headphones','Wired Headphones','Wired Headphones','Wired Headphones']}
product_type = pd.DataFrame(product_type)
bottom_5_rated_products_7['ProductType'] = product_type
bottom_5_rated_products_7

# wireless_earbuds
bottom_5_rated_products_8 = wireless_earbuds.sort_values(['Rate'],ascending = True).head()
bottom_5_rated_products_8 = bottom_5_rated_products_8.set_index('ProductName').reset_index()
bottom_5_rated_products_8
product_type = {'Product_Type':['Wireless Earbuds','Wireless Earbuds','Wireless Earbuds','Wireless Earbuds','Wireless Earbuds']}
product_type = pd.DataFrame(product_type)
bottom_5_rated_products_8['ProductType'] = product_type
bottom_5_rated_products_8

# wireless_headphones
bottom_5_rated_products_9= wireless_headphones.sort_values(['Rate'],ascending = True).head()
bottom_5_rated_products_9= bottom_5_rated_products_9.set_index('ProductName').reset_index()
bottom_5_rated_products_9
product_type = {'Product_Type':['Wireless Headphones','Wireless Headphones','Wireless Headphones','Wireless Headphones','Wireless Headphones']}
product_type = pd.DataFrame(product_type)
bottom_5_rated_products_9['ProductType'] = product_type


bottom_5_rated_products = pd.concat([bottom_5_rated_products_1,bottom_5_rated_products_2,bottom_5_rated_products_3,bottom_5_rated_products_4,bottom_5_rated_products_5,bottom_5_rated_products_6,bottom_5_rated_products_7,bottom_5_rated_products_8,bottom_5_rated_products_9])


bottom_5_rated = bottom_5_rated_products.where(bottom_5_rated_products['Reviews'] > 10).dropna().set_index('ProductName').reset_index().head(10)
bottom_5_rated


total_sales = bluetooth_speakers['Reviews'].sum() + limited_edition['Reviews'].sum() + misfit['Reviews'].sum() + mobile_accessories['Reviews'].sum() + smart_watches['Reviews'].sum() + trebel['Reviews'].sum() + wired_headphones['Reviews'].sum() + wireless_earbuds['Reviews'].sum() + wireless_headphones['Reviews'].sum()


total_products = bluetooth_speakers['ProductName'].count() + limited_edition['ProductName'].count() + misfit['ProductName'].count() + mobile_accessories['ProductName'].count()+ smart_watches['ProductName'].count() + trebel['ProductName'].count()+ wired_headphones['ProductName'].count() + wireless_earbuds['ProductName'].count()+ wireless_headphones['ProductName'].count()


print("\n Total Sales Generated only from reviews are {} from Total Products {}.\n".format(total_sales,total_products))

 Total Sales Generated only from reviews are 14975 from Total Products 196.


# bluetooth_speakers
bluetooth_speakers_total_amount = bluetooth_speakers
bluetooth_speakers_total_amount['TotalAmount'] = bluetooth_speakers_total_amount['ProductPrice'] * bluetooth_speakers_total_amount['Reviews'] 

# limited_edition
limited_edition_total_amount = limited_edition
limited_edition_total_amount['TotalAmount'] = limited_edition_total_amount['ProductPrice'] * limited_edition_total_amount['Reviews'] 

# misfit
misfit_total_amount = misfit
misfit_total_amount['TotalAmount'] = misfit_total_amount['ProductPrice'] * misfit_total_amount['Reviews'] 

# mobile_accessories
mobile_accessories_total_amount = mobile_accessories
mobile_accessories_total_amount['TotalAmount'] = mobile_accessories_total_amount['ProductPrice'] * mobile_accessories_total_amount['Reviews'] 

# smart_watches
smart_watches_total_amount = smart_watches
smart_watches_total_amount['TotalAmount'] = smart_watches_total_amount['ProductPrice'] * smart_watches_total_amount['Reviews'] 

# trebel
trebel_total_amount = trebel
trebel_total_amount['TotalAmount'] = trebel_total_amount['ProductPrice'] * trebel_total_amount['Reviews'] 

# wired_headphones
wired_headphones_total_amount = wired_headphones
wired_headphones_total_amount['TotalAmount'] = wired_headphones_total_amount['ProductPrice'] * wired_headphones_total_amount['Reviews'] 

# wireless_earbuds
wireless_earbuds_total_amount = wireless_earbuds
wireless_earbuds_total_amount['TotalAmount'] = wireless_earbuds_total_amount['ProductPrice'] * wireless_earbuds_total_amount['Reviews'] 

# wireless_headphones
wireless_headphones_total_amount = wireless_headphones
wireless_headphones_total_amount['TotalAmount'] = wireless_headphones_total_amount['ProductPrice'] * wireless_headphones_total_amount['Reviews']


total_amount = bluetooth_speakers_total_amount['TotalAmount'].sum() + limited_edition_total_amount['TotalAmount'].sum() + misfit_total_amount['TotalAmount'].sum() +  mobile_accessories_total_amount['TotalAmount'].sum()  + smart_watches_total_amount['TotalAmount'].sum() + trebel_total_amount['TotalAmount'].sum() +  wired_headphones_total_amount['TotalAmount'].sum() + wireless_headphones_total_amount['TotalAmount'].sum() + wireless_headphones_total_amount['TotalAmount'].sum()


print('Estimated Total Amount {} generated from Estimated Total Sales {} from only {} products. '.format(total_amount,total_sales,total_products))

Estimated Total Amount 20354530 generated from Estimated Total Sales 14975 from only 196 products.


#  Total Revenue

bluetooth_speakers_total_amount['TotalAmount'].sum() # 788266
limited_edition_total_amount['TotalAmount'].sum() # 407770
misfit_total_amount['TotalAmount'].sum() # 102326
mobile_accessories_total_amount['TotalAmount'].sum() # 208998
smart_watches_total_amount['TotalAmount'].sum() # 2955045
trebel_total_amount['TotalAmount'].sum() # 356450
wired_headphones_total_amount['TotalAmount'].sum() # 1203077
wireless_earbuds_total_amount['TotalAmount'].sum() # 6387030
wireless_headphones_total_amount['TotalAmount'].sum() # 7166299

7166299


#  Total Sales

print(bluetooth_speakers['Reviews'].sum())   # 434
print(limited_edition['Reviews'].sum()) # 280
print(misfit['Reviews'].sum()) # 74
print(mobile_accessories['Reviews'].sum()) # 452
print(smart_watches['Reviews'].sum()) # 1605
print(trebel['Reviews'].sum()) # 250
print(wired_headphones['Reviews'].sum()) # 2373
print(wireless_earbuds['Reviews'].sum())  # 4428
print(wireless_headphones['Reviews'].sum()) # 5079

434
280
74
452
1605
250
2373
4428
5079


product_category = {'Product_Category': ['Bluetooth Speakers','Limited Edition','Misfit','Mobile Accessories','Smart Watches','T Rebel','Wired Headphones','Wireless Earbuds','Wireless Headphones']}
product_category
product_category = pd.DataFrame(product_category)

total_revenue= {'Total_Revenue':[788266,407770,102326,208998,2955045,356450,1203077,6387030,7166299]}
total_revenue
total_revenue = pd.DataFrame(total_revenue)

total_sales= {'Total_Sales':[434,280,74,452,1605,250,2373,4428,5079]}
total_sales = pd.DataFrame(total_sales)


sales_revenue_by_category = pd.concat([product_category,total_revenue,total_sales],axis = 1)
sales_revenue_by_category


pe.bar(data_frame = sales_revenue_by_category,
      x = 'Product_Category',
      y = 'Total_Revenue',
      text = 'Total_Revenue',
      color = 'Product_Category',
      title = 'Estimated Total Revenue Generated from Different Categories of Products')


sales_revenue_by_category


pe.bar(data_frame = sales_revenue_by_category,
      x = 'Product_Category',
      y = 'Total_Sales',
      color = 'Product_Category',
      text = 'Total_Sales',
      title= 'Estimated Total Sales Generated from Different Categories of Products')


fig,axes = plt.subplots(2,1,figsize=(20,15))
fig.suptitle('Estimated Total Revenue and Total Sales Generated From Different Categories',fontsize = 20)

# Total Revenue Generated 
sns.barplot(ax=axes[0],
           x = 'Product_Category',
           y='Total_Revenue',
           data = sales_revenue_by_category.sort_values('Total_Revenue',ascending = False),
           palette = 'turbo')
axes[0].set_title('Total Revenue generated from different Categories (in Lakhs)')


#Total Sales Generated
sns.barplot(ax=axes[1],
           x = 'Product_Category',
           y='Total_Sales',
           data = sales_revenue_by_category.sort_values('Total_Revenue',ascending = False),
           palette = 'turbo')
axes[1].set_title('Estimated Total Sales Generated (only from reviews) from different Categories');


sales_revenue_by_category.sort_values('Total_Revenue',ascending = False).set_index('Product_Category').reset_index()


discount = pd.concat([bluetooth_speakers,limited_edition,misfit,mobile_accessories,smart_watches,trebel,wired_headphones,wireless_earbuds,wireless_headphones],axis = 0)
discount = discount.sort_values('Discount',ascending = True)
discount


discount['Discount'].unique()

array(['17%', '30%', '33%', '40%', '41%', '43%', '45%', '47%', '48%',
       '49%', '50%', '51%', '52%', '53%', '54%', '55%', '56%', '57%',
       '58%', '59%', '60%', '61%', '62%', '63%', '64%', '65%', '66%',
       '67%', '68%', '69%', '70%', '71%', '72%', '73%', '74%', '75%',
       '76%', '77%', '79%', '80%', '83%'], dtype=object)


discount


plt.figure(figsize=(30,6))
sns.histplot(data = discount,
            x = 'Discount')
plt.xlabel('Discount',fontsize = 20)
plt.title('Range of Discounts of Products',fontsize = 20);


discount = discount.groupby('Discount').count().iloc[:,[0]]
discount.rename(columns = {'ProductName':'Product_Count'},inplace=True)
discount.sort_values(['Product_Count'],ascending = False)


print("\n Highest Discount Percentage - {}\n".format(discount.index.max()))

 Highest Discount Percentage - 83%


print("\n Least Discount Percentage - {}\n".format(discount.index.min()))

 Least Discount Percentage - 17%


print("\n Total Number of Discount Percentage Offers - {}\n".format(discount['Product_Count'].count()))

 Total Number of Discount Percentage Offers - 41


trebel


trebel['ProductName'].iloc[10]

'TRebel Airdopes 171 - Wireless Earbuds with 6mm Drivers, 380mAh Charging cum Carrying Case, IPX4 Sweat and Water Resistance, 13H nonstop Music'


# Changing 'TRebel Airdopes 171 - Wireless Earbuds with 6mm Drivers, 380mAh Charging cum Carrying Case, IPX4 Sweat and Water Resistance, 13H nonstop Music' to 'TRebel Airdopes 171'
trebel['ProductName'].loc[10] = 'TRebel Airdopes 171'
trebel

C:\Users\Sreejith\AppData\Local\Temp\ipykernel_17028\3497724289.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


# add all df with product name and prices and then plot
prices_1 = bluetooth_speakers[['ProductName','ProductPrice']]
prices_2 = limited_edition[['ProductName','ProductPrice']]
prices_3 = misfit[['ProductName','ProductPrice']]
prices_4 = mobile_accessories[['ProductName','ProductPrice']]
prices_5 = smart_watches[['ProductName','ProductPrice']]
prices_6 = trebel[['ProductName','ProductPrice']]
prices_7 = wired_headphones[['ProductName','ProductPrice']]
prices_8 = wireless_earbuds[['ProductName','ProductPrice']]
prices_9 = wireless_headphones[['ProductName','ProductPrice']]


prices = pd.concat([prices_1,prices_2,prices_3,prices_4,prices_5,prices_6,prices_7,prices_8,prices_9])
prices


pe.scatter(data_frame = prices,
            x = 'ProductPrice',
          y = 'ProductName',
          width = 1000,
          height = 3000,
          title = ' Products Price Range')

	ProductName	ProductPrice	Discount	NumberofReviews	Rate	Review	Summary
0	Stone 1000v2	\nSale price₹ 3,999	43% off	7 reviews	★\n 5.0\n	Fantastic product	Very good product in this price range..
1	Stone 1000v2	Sale price₹ 3,999	43% off	7 reviews	★\n 5.0\n	Outstanding	Outstanding Product
2	Stone 1000v2	\nSale price₹ 3,999	43% off	7 reviews	★\n 5.0\n	Awesome	Works like a charm. Good sound quality. Visual...
3	Stone 1000v2	\nSale price₹ 3,999	43% off	7 reviews	★\n 5.0\n	Boat stone 1000 v2	Great
4	Stone 1000v2	\nSale price₹ 3,999	43% off	7 reviews	★\n 5.0\n	Quality sound	Amazing sound quality.totally premium.&.afford...
...	...	...	...	...	...	...	...
109	Stone 500	\nSale price₹ 1,999	33% off	8 reviews	★\n 4.9\n	Boat stone 500🔥	Boat stone 500 is the best speaker should buy ...
110	Stone 500	\nSale price₹ 1,999	33% off	8 reviews	★\n 4.9\n	Awesome	Great delivery, product is great too with the ...
111	Stone 500	\nSale price₹ 1,999	33% off	8 reviews	★\n 4.9\n	NaN	boAt Stone 500
112	Stone 500	\nSale price₹ 1,999	33% off	8 reviews	★\n 4.9\n	NaN	A good quality product
113	Stone 500	\nSale price₹ 1,999	33% off	8 reviews	★\n 4.9\n	NaN	boAt Stone 500

	ProductName	ProductPrice	Discount	Reviews	Rate
0	Stone 1000v2	3999	43%	7	5.0
1	Stone Grenade	1499	62%	92	4.9
2	Stone 190	1299	57%	108	4.8
3	Stone 352	1699	51%	26	4.8
4	Stone 650	1999	60%	107	4.9
5	Stone 1200F	3999	43%	38	5.0
6	Stone 135	899	55%	17	4.9
7	Stone 180	939	62%	20	4.8
8	Rugby Plus	1999	60%	11	4.6
9	Stone 500	1999	33%	8	4.9

	ProductPrice	Reviews	Rate
count	10.000000	10.000000	10.000000
mean	2033.000000	43.400000	4.860000
std	1113.155674	41.878661	0.117379
min	899.000000	7.000000	4.600000
25%	1349.000000	12.500000	4.800000
50%	1849.000000	23.000000	4.900000
75%	1999.000000	78.500000	4.900000
max	3999.000000	108.000000	5.000000

	ProductName	ProductPrice	Discount	NumberofReviews	Rate	Review	Summary
0	Rockerz 558 Sunburn Edition	\nSale price₹ 1,999	60% off	1 review	★\n 4.0\n	Good sound. Worth its price	Product is comfy, looks premium, feels great (...
1	Airdopes 383 Sunburn Edition	\nSale price₹ 2,499	50% off	9 reviews	★\n 5.0\n	Excellent battery life	Can listen for almost more than 6 hours in si...
2	Airdopes 383 Sunburn Edition	\nSale price₹ 2,499	50% off	9 reviews	★\n 5.0\n	NaN	boAt Airdopes 383 Sunburn Edition
3	Airdopes 383 Sunburn Edition	\nSale price₹ 2,499	50% off	9 reviews	★\n 5.0\n	NaN	boAt Airdopes 383 Sunburn Edition
4	Airdopes 383 Sunburn Edition	\nSale price₹ 2,499	50% off	9 reviews	★\n 5.0\n	NaN	Thanks boat
...	...	...	...	...	...	...	...
135	Rockerz 450 DC edition	\nSale price₹ 1,299	67% off	4 reviews	★\n 5.0\n	Headphone	Veerrrrrrrrry gooooooooddddddd
136	Rockerz 450 DC edition	\nSale price₹ 1,299	67% off	4 reviews	★\n 5.0\n	NaN	Rockerz 450 DC edition
137	Rockerz 450 DC edition	\nSale price₹ 1,299	67% off	4 reviews	★\n 5.0\n	NaN	Rockerz 450 DC edition
138	Rockerz 450 DC edition	\nSale price₹ 1,299	67% off	4 reviews	★\n 5.0\n	Loved it	Really really loved it ❣️
139	Rockerz 450	\nSale price₹ 1,299	67% off	1 review	★\n 5.0\n	Good	Good

	ProductName	ProductPrice	Discount	Reviews	Rate
0	Rockerz 558 Sunburn Edition	1999	60%	1	4.0
1	Airdopes 383 Sunburn Edition	2499	50%	9	5.0
2	Airdopes 131 Captain America Marvel Edition	1599	47%	43	4.9
3	Airdopes 131 Iron Man Marvel Edition	1599	47%	70	4.9
4	Bassheads 172 Sunburn Edition	349	73%	12	4.8
5	BassHeads 152 Sunburn Edition	399	69%	20	4.9
6	Airdopes 381 Sunburn Edition	2999	40%	1	5.0
7	Watch Storm - Captain America Marvel Edition	1999	67%	1	5.0
8	Airdopes 441 KKR Edition	2499	58%	1	5.0
9	Stone 190 - Captain America Marvel Edition	1299	57%	2	5.0
10	Stone SpinX 2.0 Bira Edition	2099	70%	7	4.9
11	Stone 190 - Iron Man Marvel Edition	1299	57%	3	5.0
12	Airdopes 381 Masaba Edition	2299	54%	2	5.0
13	Stone 190 - Black Panther Marvel Edition	1299	57%	1	5.0
14	Rockerz 450 - Iron Man Marvel Edition	1799	55%	8	5.0
15	Watch Storm - Black Panther Marvel Edition	1999	67%	2	5.0
16	Rockerz 550 Sunburn Edition	1499	70%	14	4.9
17	Rockerz 600 KKR Edition	2499	50%	4	5.0
18	Airdopes 441 - Masaba Edition	1799	70%	6	4.7
19	BassHeads 152 KKR Edition	549	57%	21	4.8
20	Bassheads 172 KKR Edition	499	62%	1	5.0
21	Airdopes 131 Batman DC Edition	999	67%	13	4.9
22	Airdopes 131	999	67%	1	5.0
23	Airdopes 441 Pro Special Batman Edition	2199	69%	24	5.0
24	Rockerz 450 Batman DC Edition	1299	67%	8	4.9
25	Rockerz 450 DC edition	1299	67%	4	5.0
26	Rockerz 450	1299	67%	1	5.0

BoAt Product Review Analysis Project

Data Cleaning of `Bluetooth Speakers` csv file¶

Data Cleaning of `Limited Edition` csv file¶

Data Cleaning of `Misfit` csv file¶

Data Cleaning of `Mobile Accessories` csv file¶

Data Cleaning of `Smart Watches` csv file¶

Data Cleaning of `Trebel` csv file¶

Data Cleaning of `Wired Headphones` csv file¶

Data Cleaning of `Wireless Earbuds` csv file¶

Data Cleaning of `Wireless Headphones` csv file¶

Exploratory Data Analysis [ EDA ]

Q1. Top 20 Product with Most Reviews and Discount.¶

`Airdopes 402` Has The `Most Reviews` And Has The `Highest Percentage Of Discount` And Is Best In `Wireless Earbuds Segment`.

Q2. Top 10 Products with Highest Rating and Highest Reviews.¶

`TRebel Airdopes 141` which falls in `T-Rebel` category has the Highest Rating and `Most Number of Reviews`.

Q3. Product with Most Number Of Reviews.¶

`Airdopes 131` which falls under `Wireless Earbuds` Category has the `Most Number of Reviews`.

Q4. Less Rated products.¶

`Boat Ruby Plus` which falls under `Bluetooth Speakers` category Is `Less Rated Product` Among All The Other Products

Q5. Estimated Total Sales Generated Only Through Reviewed Products From different Categories.¶

Q6. Estimated Total Amount Generated only through reviewed products from different Categories.¶

`2 Crore` Generated from `14 Thousand Customers` only from `196 Products`.¶

Q7. Overall Revenue Generated from all Categories of Products.¶

Q8. Overall Sales Generated from all Categories.¶

Q9. Highest and Lowest Discounted Products.¶

Q10. Prices Range Scatterplot.¶

Most of the BoAt products range between ₹ 199 to ₹ 3000.¶

Only 1 Product that is `T-Rebel Matrix` range in highest price category that is ₹ 5000.¶

`T-Rebel Matrix`

Conclusion

Thank You!

	ProductName	ProductPrice	Discount	NumberofReviews	Rate	Review	Summary
0	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	NaN	Thanks for the quality
1	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	Good	Good product
2	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	NaN	Misfit T50 Trimmer
3	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	Awesome product	I didn't expected this quality at this price
4	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	Great	I have used this trimmer many times after buyi...
5	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	Nice product	Premium quality
6	Misfit T50 Lite	\nSale price₹ 879	56% off	4 reviews	★\n 4.5\n	NaN	Good
7	Misfit T50 Lite	\nSale price₹ 879	56% off	4 reviews	★\n 4.5\n	NaN	Sleek design, nice performance.\nSatisfied wit...
8	Misfit T50 Lite	\nSale price₹ 879	56% off	4 reviews	★\n 4.5\n	Nice	Nice
9	Misfit T50 Lite	\nSale price₹ 879	56% off	4 reviews	★\n 4.5\n	Very nice	Good delivery, nice trimmer and boat good brand
10	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	NaN	VERY GOOD PERFORMANCE
11	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	Nice one	Very skin friendly blades. \nNormal power and ...
12	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	Best	Good grooming performance
13	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	Fantastic	Very nice
14	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	NaN	Nish
15	Misfit T200 3-in-1 Grooming Kit for Men	\nSale price₹ 1,199	70% off	2 reviews	★\n 5.0\n	NaN	Best at this segment
16	Misfit T200 3-in-1 Grooming Kit for Men	\nSale price₹ 1,199	70% off	2 reviews	★\n 5.0\n	Outstanding performance	Nice packaging and no doubt \nYou can buy it w...
17	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	NaN	Misfit T200
18	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	Very good	😀😀😀
19	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	very good	excellent item
20	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	Amazing worth taking	Great experience
21	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	Product	Products and packaging both are really good
22	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	Good Product	Used it for the first time, seemed quite smoot...
23	Misfit T150 Trimmer	\nSale price₹ 1,429	59% off	1 review	★\n 4.0\n	BoAt misfit T150 trimmer	Product is excellent but deliver time might be...
24	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	NaN	Thanks for the quality
25	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	Good	Good product
26	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	NaN	Misfit T50 Trimmer
27	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	Awesome product	I didn't expected this quality at this price
28	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	Great	I have used this trimmer many times after buyi...
29	Misfit T50 Trimmer	\nSale price₹ 989	60% off	20 reviews	★\n 4.9\n	Nice product	Premium quality
30	Misfit T50 Lite	\nSale price₹ 879	56% off	4 reviews	★\n 4.5\n	NaN	Good
31	Misfit T50 Lite	\nSale price₹ 879	56% off	4 reviews	★\n 4.5\n	NaN	Sleek design, nice performance.\nSatisfied wit...
32	Misfit T50 Lite	\nSale price₹ 879	56% off	4 reviews	★\n 4.5\n	Nice	Nice
33	Misfit T50 Lite	\nSale price₹ 879	56% off	4 reviews	★\n 4.5\n	Very nice	Good delivery, nice trimmer and boat good brand
34	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	NaN	VERY GOOD PERFORMANCE
35	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	Nice one	Very skin friendly blades. \nNormal power and ...
36	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	Best	Good grooming performance
37	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	Fantastic	Very nice
38	Misfit T30 Trimmer	\nSale price₹ 769	49% off	5 reviews	★\n 4.8\n	NaN	Nish
39	Misfit T200 3-in-1 Grooming Kit for Men	\nSale price₹ 1,199	70% off	2 reviews	★\n 5.0\n	NaN	Best at this segment
40	Misfit T200 3-in-1 Grooming Kit for Men	\nSale price₹ 1,199	70% off	2 reviews	★\n 5.0\n	Outstanding performance	Nice packaging and no doubt \nYou can buy it w...
41	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	NaN	Misfit T200
42	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	Very good	😀😀😀
43	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	very good	excellent item
44	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	Amazing worth taking	Great experience
45	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	Product	Products and packaging both are really good
46	Misfit T200	\nSale price₹ 1,699	58% off	42 reviews	★\n 4.9\n	Good Product	Used it for the first time, seemed quite smoot...
47	Misfit T150 Trimmer	\nSale price₹ 1,429	59% off	1 review	★\n 4.0\n	BoAt misfit T150 trimmer	Product is excellent but deliver time might be...

	ProductName	ProductPrice	Discount	NumberofReviews	Rate	Review	Summary
0	Mask	\nSale price₹ 499	17% off	147 reviews	★\n 4.9\n	Excellent!!	One of the most comfortable masks available in...
1	Mask	\nSale price₹ 499	17% off	147 reviews	★\n 4.9\n	Good mask	Quality is good and good to use . size is for ...
2	Mask	\nSale price₹ 499	17% off	147 reviews	★\n 4.9\n	The best in range	Super bass with very good spec.... amazing ne...
3	Mask	\nSale price₹ 499	17% off	147 reviews	★\n 4.9\n	Excellent Product	Top notch product amongst other items in the m...
4	Mask	\nSale price₹ 499	17% off	147 reviews	★\n 4.9\n	Excellent product	Very comfortable..
...	...	...	...	...	...	...	...
113	Dual QC-PD Port Rapid Car Charger With Type C...	\nSale price₹ 599	60% off	5 reviews	★\n 5.0\n	Awesome product	Best car charger
114	Dual QC-PD Port Rapid Car Charger With Type C...	\nSale price₹ 599	60% off	5 reviews	★\n 5.0\n	Its response is very good	It's data cable and fast charging is too much ...
115	Dual QC-PD Port Rapid Car Charger With Type C...	\nSale price₹ 599	60% off	5 reviews	★\n 5.0\n	NaN	boAt Dual QC-PD Port Rapid Car Charger With Ty...
116	Dual QC-PD Port Rapid Car Charger With Type C...	\nSale price₹ 599	60% off	5 reviews	★\n 5.0\n	NaN	boAt Dual QC-PD Port Rapid Car Charger With Ty...
117	Dual QC-PD Port Rapid Car Charger With Type C...	\nSale price₹ 599	60% off	5 reviews	★\n 5.0\n	Very good Product	Its great product but just lower the price a bit.

	ProductName	ProductPrice	Discount	Reviews	Rate
0	Mask	499	17%	147	4.9
1	Deuce 330	299	57%	4	5.0
2	LTG 500 Indestructible Apple Certified Lightni...	899	49%	26	4.8
3	Micro USB 150 1.5 Meter	499	50%	2	5.0
4	A400 USB Type C Data Cable	349	65%	15	4.9
5	Type C C400	499	50%	6	4.8
6	Rugged V3 Micro USB 1.5 Meter	299	63%	63	4.9
7	Deuce USB 300	299	57%	76	4.8
8	Micro USB 500 Cable 1.5 Meter	279	65%	25	4.9
9	LTG 500 Indestructible Apple Certified Lightni...	799	47%	48	4.8
10	Energyshroom PB10	949	53%	1	5.0
11	Energyshroom PB9	949	53%	2	5.0
12	Dual Port Rapid Car Charger (Qualcomm Certified)	449	50%	7	5.0
13	Micro USB 100 1 Meter	199	72%	23	4.9
14	Dual QC-PD Port Rapid Car Charger With Power D...	699	53%	2	5.0
15	Dual QC-PD Port Rapid Car Charger With Type C ...	599	60%	5	5.0

	ProductName	ProductPrice	Discount	NumberofReviews	Rate	Review	Summary
0	Wave Select	\nSale price₹ 2,099	70% off	13 reviews	★\n 5.0\n	Very good	Always satisfied with your product
1	Wave Select	\nSale price₹ 2,099	70% off	13 reviews	★\n 5.0\n	great product	excellent
2	Wave Select	\nSale price₹ 2,099	70% off	13 reviews	★\n 5.0\n	Good Product	I just love it
3	Wave Select	\nSale price₹ 2,099	70% off	13 reviews	★\n 5.0\n	NaN	boAt Wave Select \| Best Sports Edition Smartwa...
4	Wave Select	\nSale price₹ 2,099	70% off	13 reviews	★\n 5.0\n	NaN	boAt Wave Select \| Best Sports Edition Smartwa...
...	...	...	...	...	...	...	...
191	TRebel Watch Straps	\nSale price₹ 399	56% off	1 review	★\n 5.0\n	NaN	nice strap
192	Wave Voice	\nSale price₹ 2,699	55% off	1 review	★\n 5.0\n	NaN	I asked for exchange, I still didn't get response
193	Storm RTL	\nSale price₹ 2,999	50% off	3 reviews	★\n 5.0\n	Best watch	Best watch in world
194	Storm RTL	\nSale price₹ 2,999	50% off	3 reviews	★\n 5.0\n	Watch	Nice
195	Storm RTL	\nSale price₹ 2,999	50% off	3 reviews	★\n 5.0\n	Very good quality	Good brand

	ProductName	ProductPrice	Discount	Reviews	Rate
0	Wave Select	2099	70%	13	5.0
1	Storm Call	1799	77%	23	4.7
2	Wave Beat	1499	75%	5	4.6
3	Wave Neo	1499	75%	20	4.7
4	Wave Connect	2999	57%	88	4.9
5	Xtend‌	2699	66%	146	4.8
6	Wave Call	1799	77%	41	4.9
7	Wave Lite	1699	76%	55	4.9
8	Watch Flash	1399	80%	48	4.8
9	Storm	1499	75%	1008	4.8
10	Wave Style	1449	76%	17	4.7
11	Xplorer‌‌ O2	3999	33%	29	4.8
12	Wave Pro	1499	79%	12	5.0
13	Blaze	3499	50%	20	5.0
14	Matrix	3499	71%	11	5.0
15	Xtend Talk	2999	57%	7	4.9
16	Storm Pro	2499	64%	10	5.0
17	Xtend Sport	2499	64%	7	4.9
18	Mystiq	2499	64%	5	4.8
19	Wave Pro 47	1799	74%	15	4.9
20	TRebel Matrix	4999	58%	1	5.0
21	TRebel Blaze	3999	43%	6	5.0
22	TRebel Xtend‌	3499	56%	3	5.0
23	Wave Play	1999	67%	1	4.0
24	Watch Storm - Iron Man Marvel Edition	1999	67%	2	5.0
25	Watch Storm - Black Panther Marvel Edition	1999	67%	2	5.0
26	Wave Ultima	2999	50%	4	5.0
27	Watch Storm - Captain America Marvel Edition	1999	67%	1	5.0
28	TRebel Watch Straps	399	56%	1	5.0
29	Wave Voice	2699	55%	1	5.0
30	Storm RTL	2999	50%	3	5.0

	ProductName	ProductPrice	Discount	Reviews	Rate
0	TRebel Blaze	3999	43%	6	5.0
1	TRebel Rockerz 333	1599	60%	6	5.0
2	TRebel Airdopes 381	2199	56%	12	4.8
3	TRebel Airdopes 402	1999	67%	9	4.8
4	TRebel BassHeads 100	399	60%	17	4.9
5	TRebel Xtend‌	3499	56%	3	5.0
6	TRebel Rockerz 450	1499	62%	1	5.0
7	TRebel BassHeads 103	499	61%	11	5.0
8	TRebel Airdopes 141	1499	67%	63	5.0
9	TRebel BassHeads 102	399	69%	7	5.0
10	TRebel Airdopes 171 - Wireless Earbuds with 6m...	1799	70%	3	5.0
11	TRebel Matrix	4999	58%	1	5.0
12	TRebel Rockerz 330 Pro	1799	40%	3	5.0
13	Trebel Rockerz 255 Pro+	1599	60%	7	4.7
14	TRebel Airdopes 181	1599	47%	4	4.8
15	TRebel Watch Straps	399	56%	1	5.0
16	TRebel Rockerz 235 V2	1199	60%	20	4.8
17	TRebel Airdopes 131	1299	57%	56	4.7
18	TRebel BassHeads 152	499	61%	11	5.0
19	TRebel Airdopes 441 Pro	2999	50%	5	4.8
20	TRebel Rockerz 255 Pro	1499	57%	4	5.0

	ProductName	ProductPrice	Discount	NumberofReviews	Rate	Review	Summary
0	Bassheads 900	\nSale price₹ 849	66% off	169 reviews	★\n 4.8\n	Overall good experience.	good.
1	Bassheads 900	\nSale price₹ 849	66% off	169 reviews	★\n 4.8\n	Good	Good product
2	Bassheads 900	\nSale price₹ 849	66% off	169 reviews	★\n 4.8\n	NaN	Useful product
3	Bassheads 900	\nSale price₹ 849	66% off	169 reviews	★\n 4.8\n	NaN	Best quality ever
4	Bassheads 900	\nSale price₹ 849	66% off	169 reviews	★\n 4.8\n	NaN	The build is excellent. You won't get any bett...
...	...	...	...	...	...	...	...
148	BassHeads 225	\nSale price₹ 699	30% off	7 reviews	★\n 4.9\n	NaN	So far good
149	BassHeads 225	\nSale price₹ 699	30% off	7 reviews	★\n 4.9\n	Good earphones	Good earphones
150	BassHeads 225	\nSale price₹ 699	30% off	7 reviews	★\n 4.9\n	❤️❤️❤️	Awesome product...heavy bass and better clarit...
151	BassHeads 225	\nSale price₹ 699	30% off	7 reviews	★\n 4.9\n	Nice product	Boat never compromised on quality... I liked t...
152	BassHeads 225	\nSale price₹ 699	30% off	7 reviews	★\n 4.9\n	good	good

	ProductName	ProductPrice	Discount	Reviews	Rate
0	Bassheads 900	849	66%	169	4.8
1	BassHeads 242	599	60%	32	4.9
2	TRebel BassHeads 242	599	60%	371	4.8
3	BassHeads 152	449	65%	73	4.8
4	BassHeads 172	499	62%	135	4.8
5	BassHeads 100	399	60%	423	4.8
6	BassHeads 102	449	65%	157	4.8
7	BassHeads 103	499	61%	552	4.8
8	BassHeads 220	699	30%	45	4.8
9	TRebel BassHeads 100	399	60%	17	4.9
10	TRebel BassHeads 152	499	61%	11	5.0
11	BassHeads 104	449	55%	66	4.7
12	TRebel BassHeads 102	399	69%	7	5.0
13	TRebel BassHeads 103	499	61%	11	5.0
14	Bassheads 192 - Make in India	449	55%	1	5.0
15	BassHeads 225	399	60%	301	4.8
16	BassHeads 228	699	41%	1	5.0
17	BassHeads 110	449	55%	1	5.0

	ProductName	ProductPrice	Discount	NumberofReviews	Rate	Review	Summary
0	Airdopes 131	\nSale price₹ 999	67% off	1298 reviews	★\n 4.8\n	Airdopes 131	Good product, sound quality is also good
1	Airdopes 131	\nSale price₹ 999	67% off	1298 reviews	★\n 4.8\n	Loved the product	Product is simply awesome. Loved this.
2	Airdopes 131	\nSale price₹ 999	67% off	1298 reviews	★\n 4.8\n	Airdopes not fit in years	Worst product no exchange no return policy. No...
3	Airdopes 131	\nSale price₹ 999	67% off	1298 reviews	★\n 4.8\n	Airdopes 131	So cool
4	Airdopes 131	\nSale price₹ 999	67% off	1298 reviews	★\n 4.8\n	best aipods	good quality
...	...	...	...	...	...	...	...
204	Airdopes 131 Iron Man Marvel Edition	\nSale price₹ 1,599	47% off	70 reviews	★\n 4.9\n	Jhakkas 🔥💯🤘😎	It's awesome thank you boAt 👍
205	Airdopes 131 Iron Man Marvel Edition	\nSale price₹ 1,599	47% off	70 reviews	★\n 4.9\n	Super	Useful Thank u boat
206	Airdopes 131 Iron Man Marvel Edition	\nSale price₹ 1,599	47% off	70 reviews	★\n 4.9\n	Awesome product	Worthy product according to price. And persona...
207	Airdopes 131 Iron Man Marvel Edition	\nSale price₹ 1,599	47% off	70 reviews	★\n 4.9\n	Very good	Very good
208	Airdopes 131 Iron Man Marvel Edition	\nSale price₹ 1,599	47% off	70 reviews	★\n 4.9\n	This is amazing	Sound is too good

	ProductName	ProductPrice	Discount	Reviews	Rate
0	Airdopes 131	999	67%	1298	4.8
1	Airdopes 131 PRO	1199	60%	157	4.9
2	Airdopes 411 ANC	2499	50%	68	4.8
3	Airdopes 121 PRO	1499	50%	40	4.9
4	Airdopes 141	1499	67%	598	4.8
5	Airdopes 121 v2	1299	57%	562	4.8
6	Airdopes 402	1499	75%	479	4.8
7	Airdopes 115	999	67%	82	4.9
8	TRebel Airdopes 141	1499	67%	63	5.0
9	TRebel Airdopes 131	1299	57%	56	4.7
10	Airdopes 161	1381	45%	119	4.9
11	Airdopes 441	1999	67%	289	4.9
12	Airdopes 141 Pro	1699	43%	37	4.9
13	Airdopes 381	1999	60%	122	4.8
14	Airdopes 413 ANC	2499	50%	14	5.0
15	Airdopes 138	1499	50%	7	5.0
16	TRebel Airdopes 381	2199	56%	12	4.8
17	TRebel Airdopes 402	1999	67%	9	4.8
18	Airdopes 171	1499	75%	69	4.8
19	Airdopes 181	1599	47%	36	4.9
20	Airdopes 451v2	2999	50%	33	4.8
21	Airdopes 131 Captain America Marvel Edition	1599	47%	43	4.9
22	Airdopes 601 ANC	2999	70%	3	5.0
23	Airdopes 441 Pro	2999	50%	154	4.8
24	TRebel Airdopes 441 Pro	2999	50%	5	4.8
25	Airdopes 500 ANC	4499	55%	3	5.0
26	Airdopes 131 Iron Man Marvel Edition	1599	47%	70	4.9

	ProductType	Total Products
0	Bluetooth Speakers	2
1	Mobile Accessories	1
2	Smart Watches	2
3	Wired Headphones	5
4	Wireless Earbuds	5
5	Wireless Headphones	5

	ProductName	ProductPrice	Discount	NumberofReviews	Rate	Review	Summary
0	Rockerz 235 V2	\nSale price₹ 1,183	60% off	1067 reviews	★\n 4.8\n	Good	Sonda good
1	Rockerz 235 V2	\nSale price₹ 1,183	60% off	1067 reviews	★\n 4.8\n	Best	The best product ever received.
2	Rockerz 235 V2	\nSale price₹ 1,183	60% off	1067 reviews	★\n 4.8\n	NaN	boAt Rockerz 235 V2 \| Bluetooth Stereo Wireles...
3	Rockerz 235 V2	\nSale price₹ 1,183	60% off	1067 reviews	★\n 4.8\n	NaN	Supar
4	Rockerz 235 V2	\nSale price₹ 1,183	60% off	1067 reviews	★\n 4.8\n	NaN	The product is perfect.
...	...	...	...	...	...	...	...
282	Trebel Rockerz 255 Pro+	\nSale price₹ 1,599	60% off	7 reviews	★\n 4.7\n	NaN	Trebel Rockerz 255 Pro+
283	Trebel Rockerz 255 Pro+	\nSale price₹ 1,599	60% off	7 reviews	★\n 4.7\n	Awesome product	I am using this since 10 months and fully sati...
284	Trebel Rockerz 255 Pro+	\nSale price₹ 1,599	60% off	7 reviews	★\n 4.7\n	Awesome	Sound quality good best Material
285	Trebel Rockerz 255 Pro+	\nSale price₹ 1,599	60% off	7 reviews	★\n 4.7\n	Best service	Best service and best product very staisfied
286	Trebel Rockerz 255 Pro+	\nSale price₹ 1,599	60% off	7 reviews	★\n 4.7\n	Worthy deal except the ipl tickets :\|	Haven't heard my friends saying," tu earphones...

	ProductName	ProductPrice	Discount	Reviews	Rate
0	Rockerz 235 V2	1183	60%	1067	4.8
1	Rockerz 255 Pro+	1299	67%	401	4.8
2	Rockerz 245 V2	1299	57%	206	4.8
3	Rockerz 330	1299	57%	266	4.8
4	Rockerz 333	1399	65%	186	4.9
5	Rockerz 255	1299	57%	228	4.8
6	Rockerz 550	1999	60%	345	4.8
7	Rockerz 255 Pro	1399	60%	383	4.8
8	Rockerz 103 Pro	999	60%	64	4.9
9	Rockerz 450	1499	62%	267	4.8
10	Rockerz 255R	1299	57%	29	4.9
11	Rockerz 333 Pro	1799	40%	157	4.9
12	Rockerz 260	1299	48%	99	4.9
13	Rockerz 335	1699	57%	350	4.8
14	Rockerz 510	1299	67%	142	4.9
15	Rockerz 370	1199	52%	79	4.8
16	Rockerz 255 Neo	1299	57%	95	4.8
17	Rockerz 261	849	83%	67	4.8
18	Rockerz 333 ANC	1899	68%	48	5.0
19	Rockerz 385 V2	1499	70%	73	4.8
20	Rockerz 235 Pro	999	50%	48	4.9
21	Rockerz 255F	1299	57%	46	4.8
22	Rockerz 260 v1	1199	52%	40	4.9
23	Rockerz 330 Pro	1799	40%	47	4.9
24	Rockerz 400	1499	50%	95	4.8
25	Rockerz 245 Pro	1299	48%	16	4.8
26	Rockerz 205 Pro	999	60%	10	4.9
27	TRebel Rockerz 255 Pro	1499	57%	4	5.0
28	Rockerz 255 Pro Made in India	1399	60%	24	4.9
29	TRebel Rockerz 235 V2	1199	60%	20	4.8
30	Rockerz 330 ANC	1999	67%	12	4.7
31	Rockerz 600	1699	57%	35	4.8
32	Rockerz 375	999	75%	11	4.9
33	Rockerz 518	1599	60%	44	4.9
34	Nirvanaa 751 ANC	3999	50%	27	4.7
35	Rockerz 450R	1499	62%	10	5.0
36	Rockerz 425	1499	50%	22	4.9
37	Rockerz 450 Batman DC Edition	1299	67%	8	4.9
38	Rockerz 255 ARC	1299	48%	1	4.0
39	Trebel Rockerz 255 Pro+	1599	60%	7	4.7

	ProductName	Reviews	Rate	ProductType
0	TRebel Airdopes 141	63.0	5.0	TRebel
1	TRebel Airdopes 141	63.0	5.0	Wireless Earbuds
2	Rockerz 333 ANC	48.0	5.0	Wireless Headphones
3	Stone 1200F	38.0	5.0	Bluetooth Speakers
4	Airdopes 441 Pro Special Batman Edition	24.0	5.0	Limited Edition
5	Blaze	20.0	5.0	Smart Watches
6	Airdopes 413 ANC	14.0	5.0	Wireless Earbuds
7	Wave Select	13.0	5.0	Smart Watches
8	Wave Pro	12.0	5.0	Smart Watches
9	Matrix	11.0	5.0	Smart Watches

	ProductName	ProductPrice	Discount	Reviews	Rate	ProductType
0	Rugby Plus	1999.0	60%	11.0	4.6	Bluetooth Speakers
1	Stone 190	1299.0	57%	108.0	4.8	Bluetooth Speakers
2	Stone 352	1699.0	51%	26.0	4.8	Bluetooth Speakers
3	Stone 180	939.0	62%	20.0	4.8	Bluetooth Speakers
4	Stone Grenade	1499.0	62%	92.0	4.9	Bluetooth Speakers
5	Bassheads 172 Sunburn Edition	349.0	73%	12.0	4.8	Limited Edition
6	BassHeads 152 KKR Edition	549.0	57%	21.0	4.8	Limited Edition
7	Misfit T50 Trimmer	989.0	60%	20.0	4.9	Misfit
8	Misfit T200	1699.0	58%	42.0	4.9	Misfit
9	LTG 500 Indestructible Apple Certified Lightni...	899.0	49%	26.0	4.8	Mobile Accessories

	Product_Category	Total_Revenue	Total_Sales
0	Bluetooth Speakers	788266	434
1	Limited Edition	407770	280
2	Misfit	102326	74
3	Mobile Accessories	208998	452
4	Smart Watches	2955045	1605
5	T Rebel	356450	250
6	Wired Headphones	1203077	2373
7	Wireless Earbuds	6387030	4428
8	Wireless Headphones	7166299	5079

	ProductName	ProductPrice	Discount	Reviews	Rate	TotalAmount
0	Mask	499	17%	147	4.9	73353
8	BassHeads 220	699	30%	45	4.8	31455
11	Xplorer‌‌ O2	3999	33%	29	4.8	115971
9	Stone 500	1999	33%	8	4.9	15992
23	Rockerz 330 Pro	1799	40%	47	4.9	84553
...	...	...	...	...	...	...
1	Storm Call	1799	77%	23	4.7	41377
6	Wave Call	1799	77%	41	4.9	73759
12	Wave Pro	1499	79%	12	5.0	17988
8	Watch Flash	1399	80%	48	4.8	67152
17	Rockerz 261	849	83%	67	4.8	56883

	ProductName	ProductPrice	Discount	Reviews	Rate	TotalAmount
0	TRebel Blaze	3999	43%	6	5.0	23994
1	TRebel Rockerz 333	1599	60%	6	5.0	9594
2	TRebel Airdopes 381	2199	56%	12	4.8	26388
3	TRebel Airdopes 402	1999	67%	9	4.8	17991
4	TRebel BassHeads 100	399	60%	17	4.9	6783
5	TRebel Xtend‌	3499	56%	3	5.0	10497
6	TRebel Rockerz 450	1499	62%	1	5.0	1499
7	TRebel BassHeads 103	499	61%	11	5.0	5489
8	TRebel Airdopes 141	1499	67%	63	5.0	94437
9	TRebel BassHeads 102	399	69%	7	5.0	2793
10	TRebel Airdopes 171 - Wireless Earbuds with 6m...	1799	70%	3	5.0	5397
11	TRebel Matrix	4999	58%	1	5.0	4999
12	TRebel Rockerz 330 Pro	1799	40%	3	5.0	5397
13	Trebel Rockerz 255 Pro+	1599	60%	7	4.7	11193
14	TRebel Airdopes 181	1599	47%	4	4.8	6396
15	TRebel Watch Straps	399	56%	1	5.0	399
16	TRebel Rockerz 235 V2	1199	60%	20	4.8	23980
17	TRebel Airdopes 131	1299	57%	56	4.7	72744
18	TRebel BassHeads 152	499	61%	11	5.0	5489
19	TRebel Airdopes 441 Pro	2999	50%	5	4.8	14995
20	TRebel Rockerz 255 Pro	1499	57%	4	5.0	5996

BoAt Product Review Analysis Project

Data Cleaning of Bluetooth Speakers csv file¶

Data Cleaning of Limited Edition csv file¶

Data Cleaning of Misfit csv file¶

Data Cleaning of Mobile Accessories csv file¶

Data Cleaning of Smart Watches csv file¶

Data Cleaning of Trebel csv file¶

Data Cleaning of Wired Headphones csv file¶

Data Cleaning of Wireless Earbuds csv file¶

Data Cleaning of Wireless Headphones csv file¶

Exploratory Data Analysis [ EDA ]

Q1. Top 20 Product with Most Reviews and Discount.¶

Airdopes 402 Has The Most Reviews And Has The Highest Percentage Of Discount And Is Best In Wireless Earbuds Segment.

Q2. Top 10 Products with Highest Rating and Highest Reviews.¶

TRebel Airdopes 141 which falls in T-Rebel category has the Highest Rating and Most Number of Reviews.

Q3. Product with Most Number Of Reviews.¶

Airdopes 131 which falls under Wireless Earbuds Category has the Most Number of Reviews.

Q4. Less Rated products.¶

Boat Ruby Plus which falls under Bluetooth Speakers category Is Less Rated Product Among All The Other Products

Q5. Estimated Total Sales Generated Only Through Reviewed Products From different Categories.¶

Q6. Estimated Total Amount Generated only through reviewed products from different Categories.¶

2 Crore Generated from 14 Thousand Customers only from 196 Products.¶

Q7. Overall Revenue Generated from all Categories of Products.¶

Q8. Overall Sales Generated from all Categories.¶

Q9. Highest and Lowest Discounted Products.¶

Q10. Prices Range Scatterplot.¶

Most of the BoAt products range between ₹ 199 to ₹ 3000.¶

Only 1 Product that is T-Rebel Matrix range in highest price category that is ₹ 5000.¶

T-Rebel Matrix

Conclusion

Thank You!

Data Cleaning of `Bluetooth Speakers` csv file¶

Data Cleaning of `Limited Edition` csv file¶

Data Cleaning of `Misfit` csv file¶

Data Cleaning of `Mobile Accessories` csv file¶

Data Cleaning of `Smart Watches` csv file¶

Data Cleaning of `Trebel` csv file¶

Data Cleaning of `Wired Headphones` csv file¶

Data Cleaning of `Wireless Earbuds` csv file¶

Data Cleaning of `Wireless Headphones` csv file¶

`Airdopes 402` Has The `Most Reviews` And Has The `Highest Percentage Of Discount` And Is Best In `Wireless Earbuds Segment`.

`TRebel Airdopes 141` which falls in `T-Rebel` category has the Highest Rating and `Most Number of Reviews`.

`Airdopes 131` which falls under `Wireless Earbuds` Category has the `Most Number of Reviews`.

`Boat Ruby Plus` which falls under `Bluetooth Speakers` category Is `Less Rated Product` Among All The Other Products

`2 Crore` Generated from `14 Thousand Customers` only from `196 Products`.¶

Only 1 Product that is `T-Rebel Matrix` range in highest price category that is ₹ 5000.¶

`T-Rebel Matrix`